{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Election Poll Data Analysis\n",
    "\n",
    "In this project, we'll look at the opinion poll data from the upcoming 2016 General Election. In the process, we'll try to answer the following questions:\n",
    "\n",
    "- Who was being polled and what was their party affiliation?\n",
    "- Did the poll results favour Clinton or Trump?\n",
    "- How did voter sentiment change over time?\n",
    "- Can we see an effect in the polls from the debates?\n",
    "\n",
    "So let's get started with the imports!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Analysis imports\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "\n",
    "#Visualisation imports\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "#Imports to grab and parse data from the web\n",
    "import requests\n",
    "\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll grab the poll data from _HuffPost Pollster_, and we'll use StringIO to prevent errors with pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#url for the poll data\n",
    "url = \"http://elections.huffingtonpost.com/pollster/2016-general-election-trump-vs-clinton.csv\"\n",
    "\n",
    "#Using requests to get the data in text form\n",
    "source = requests.get(url).text\n",
    "\n",
    "#Using String IO to prevent IO errors with pandas\n",
    "poll_data = StringIO(source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the csv object into a pandas dataframe\n",
    "poll_df = pd.read_csv(poll_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1522 entries, 0 to 1521\n",
      "Data columns (total 17 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Pollster                1522 non-null   object \n",
      " 1   Start Date              1522 non-null   object \n",
      " 2   End Date                1522 non-null   object \n",
      " 3   Entry Date/Time (ET)    1522 non-null   object \n",
      " 4   Number of Observations  1013 non-null   float64\n",
      " 5   Population              1522 non-null   object \n",
      " 6   Mode                    1522 non-null   object \n",
      " 7   Trump                   1522 non-null   float64\n",
      " 8   Clinton                 1522 non-null   float64\n",
      " 9   Other                   1098 non-null   float64\n",
      " 10  Undecided               1460 non-null   float64\n",
      " 11  Pollster URL            1522 non-null   object \n",
      " 12  Source URL              1522 non-null   object \n",
      " 13  Partisan                1522 non-null   object \n",
      " 14  Affiliation             1522 non-null   object \n",
      " 15  Question Text           661 non-null    object \n",
      " 16  Question Iteration      1522 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(11)\n",
      "memory usage: 202.3+ KB\n"
     ]
    }
   ],
   "source": [
    "poll_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Trump</th>\n",
       "      <th>Clinton</th>\n",
       "      <th>Other</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>940.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>41.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Likely Voters - Democrat</td>\n",
       "      <td>Internet</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Likely Voters - Republican</td>\n",
       "      <td>Internet</td>\n",
       "      <td>82.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Likely Voters - independent</td>\n",
       "      <td>Internet</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:10:06Z</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>43.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.investors.com/politics/ibd-tipp-pre...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pollster  Start Date    End Date  Entry Date/Time (ET)  \\\n",
       "0  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "1  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "2  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "3  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "4       IBD/TIPP  2016-11-04  2016-11-07  2016-11-08T12:10:06Z   \n",
       "\n",
       "   Number of Observations                   Population        Mode  Trump  \\\n",
       "0                   940.0                Likely Voters    Internet   41.0   \n",
       "1                     NaN     Likely Voters - Democrat    Internet    6.0   \n",
       "2                     NaN   Likely Voters - Republican    Internet   82.0   \n",
       "3                     NaN  Likely Voters - independent    Internet   38.0   \n",
       "4                  1107.0                Likely Voters  Live Phone   43.0   \n",
       "\n",
       "   Clinton  Other  Undecided  \\\n",
       "0     45.0    2.0        8.0   \n",
       "1     89.0    0.0        4.0   \n",
       "2      7.0    2.0        6.0   \n",
       "3     43.0    4.0        7.0   \n",
       "4     41.0    4.0        5.0   \n",
       "\n",
       "                                        Pollster URL  \\\n",
       "0  https://elections.huffingtonpost.com/pollster/...   \n",
       "1  https://elections.huffingtonpost.com/pollster/...   \n",
       "2  https://elections.huffingtonpost.com/pollster/...   \n",
       "3  https://elections.huffingtonpost.com/pollster/...   \n",
       "4  https://elections.huffingtonpost.com/pollster/...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "1  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "2  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "3  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "4  http://www.investors.com/politics/ibd-tipp-pre...  Nonpartisan        None   \n",
       "\n",
       "                                       Question Text  Question Iteration  \n",
       "0  As you may know, there will be a presidential ...                   1  \n",
       "1  As you may know, there will be a presidential ...                   1  \n",
       "2  As you may know, there will be a presidential ...                   1  \n",
       "3  As you may know, there will be a presidential ...                   1  \n",
       "4                                                NaN                   1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who was being polled and what was their party affiliation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quickly visualising the affiliations of the different pollsters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17f6a5c90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Affiliation',data=poll_df,kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the polls have no affiliation; though there's stronger affiliation for Democrats than for Republicans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17f6a6a70>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Affiliation',data=poll_df,hue='Population',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there's a strong sample of registered voters that are not affiliated, we can hope that the poll data is a good representation of the upcoming elections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17f6a6830>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HzmujFknqdW2NiB8Efhv4u+b+fOD65vaVwP5AAldn5jDwSETMjohNMvOx8VY8NDTE4ODgi9oHBgYmq/YquvWpm5nST5hZfdXUNFmf0VaCODO/FBFbdzT1NYELsALYCJgHLO9YZqR93CDu7++f8jtoN9OxT93MlH7CzOqrXp519ce61R235wJPAE81t0e3S9KMsq6C+DsRsbC5fRCwFLgJOCAiZkXElsCszHx8HdUjST1jXV197STgwojYABgELs/MVRGxFLiFckBYtI5qkaSe0loQZ+bDwO7N7fsov5AYvcxiYHFbNUjSVOAJHZJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJLWmZXPr6pdwlprs/bZra1ZkkaZvf56nHvS12qXsVZOOOvQ1tbtiFiSKqs+Io6IWcD5wI7AEHBsZj5QtypJWnd6YUT8VmBOZr4B+GPgrLrlSNK61QtBvCdwFUBm3grsUrccad1aPTRUu4S1NpVr7yV9w8PDVQuIiIuAL2Xmlc39R4BtM3Nlt+XvuOOOx4DvrcMSJWksj8+fP//Al7uS6nPEwFPA3I77s8YKYYD58+dv0n5JkrTu9MLUxE3AwQARsTtwV91yJGnd6oUR8VeA/SLiZqAPOLpyPZK0TlWfI5akma4XpiYkaUYziCWpMoNYkirrhT/WtS4itgYuy8zdx3j8OOBzmfl8C699QmaeO0nrWggcn5mHj2q/DDgyM59by3X+X+AeYBh4BXBJZn4qIq5rXu/el1n6Wmnet9uBpym/LR9u2tcH7qecFv8YcHPzlPWB9YAjMvOhZtkvUP4A/ErgTGCrZpnvAx/KzEfHef2Hgf8CnAhck5m3d1lmIdPkPYmInYG3ZuZHI+IdwPuB1ZSc+JvMvLhj2Y8Afwhsk5nPRsRRlG31CeCjmfnfm+Va2bciYktgx8yc0BWEIuLRzNx0VNt1wIaUzxfASuC9wOvo8p62yRFxcSpl52zDaS2t92cy8/C12eE7XJOZCzNzb2ABcFJE/OLkVPeyLQMepNQ14i2Ump8EftzUvjAz9wA+B5wEEBFzKJ/xZ4AvA19ultsL+CxwRUS85PuemX/eLYRf4jlT8T15M2WbHAAcDxyamQuB/YDfbcJ5xHuAy4CfC6vMfHQkhBtt7Vv7AHtMwnqOzMy9m+38ZeCPJmGda2xGjIhHNEfAO4HfAOYB7wD2BTalfKjeGhF/BuxF+fCcnZlfbJ73I2Bj4PPAAZQj6XbAX2TmkojYATiH8hO85cAxwAnAxhFx/qgP52T362FgB+A7lFHC0xHxR8Aq4HLgbyijqmeA4zLz++Osbm7zvJGTak6PiF+mjCiPyMxlEXEW5dR0gEsz85MRsYRy0aatgc2AozLz283O+6FmnTdm5h+vRRcvBI4ErmvuHwP8zzGW3Qr4SXN7H+BaYD7wZGb+w8hCmfn/IuJB4E0RsQDYBnhN8/w/zMxvjCzb9O0yyufkYDree+BhYKOIuJaff+//han3nuzSbNevASc3Bzoy85mm9k8DX4yIf2ueuxA4KiL+jnKdmBuabzHXABcDv9+87r9FxN2UcwR2BbYAngX+o/n/A81272+286HAlsBhzfa9oHnOZsBXgdOb19uw+dnrQ7x43/tps41/nXIg7x9n+47YuHkewPYRcSXlM/G1zFwcETsBn2r6/izwPsqB/vOUb1jbAbdn5h9ExEbAZ4BXN+v7QGaOeY7ETBwR356Z+wLfpHyIPwM8ChweEQdRvmrtCewN/I+OUcjnm+etAjbKzDdTRmYjH+ILgUXNCOIfgY9k5scpI7bWQrjD88CXgLc3999F2RnOBM5p6joT+PMuz90nIq6LiGuAS4D3Z+bIB/LrmbkPcCXwOxHxZkpo7U7Z8d/VHIQAvpeZB1A+rMdFxMbAx4Dfarbpr0TEfmvRt68ACyLiFRGxGbBpc10SKAe66yLi280BaQ4lIAEOAa4AtqXsjKMtowQAwFBmHgR8kPKVeyzd3vvXM+q9b9qnzHvSBPuPmumfbturc1ttTpl+2IkyYHnvGNtqFWXq6LXN/d2AxcDSpv0EyvTSo5m5PzBI2f8ObrbboZQAvrXpw66UKYNVzTa7NDO/Spd9D3gb5WJiuwOnUA6e3VzcsZ1fC5zRtM+hXJBsr6ZOmtc5ITMXUK4YeXbT/jrKQWdX4OCI2JTyTeBbzUj7OOCvx3h9YIaNiBvfaf7/fcoIp9MOwPxmBAxlznHr5nZ2LHdnxzrmNLcHgPMjYuR5909WwWvgIuCvI+JeIDNzebNDnhoRJ1NGDN3m6q4ZZz7sjub/j1K21wCwtNlhn4+IW4Ffa5bp3LZ7AL8KbAL8Y7Nd5lJGDd9ck05l5nMR8feUHWMryrTCiB9n5sJmimEJ8FxHYG2ZmY9ExBa88D522r6pZetRtc/psuyIO7ssN4+x3/up8p5sQQkxgH+nbJORbxZQttUjEfGq5v67I+IIyhzyeHOpI9Mzqykj3x2AnSnfBi6m9H9knv4JYGTu+yeU7ftj4PURsTflcgjdRrbd9r2nKX9foPkMjPWN48jR8+3Neu7OzKHm/sg3kc0z887m9g28cAB9IDNXNMv+sKl7B8rB9HebZTYe4/WBmTki7nYGy2rKtrgXuLY5su5D+YPJgx3LjLeOpLypCylH5Cua9r6XX/LEZOb9zet9mHL0htKnk5u6/hvwxTVc7ei+DtJ8BW7+aPZGXgie0cs+RAmA/ZrX/xRwK2vnIuAIShj/n9EPNqOk44C3RcQhEfFfeeF0+ZuBTSPiZ//EQkQcSAml68eofSzdlltB9/d+Kr0n+wFXN8ucA5wREfOadf4CZaR4HmVu+JnM3D8zD6QcyHah7B+/0Dz/lR2vN7JvLaeMHO+lTDH9kDLd9DDwg3H6ehTwRGa+m3KJ3A0joq9jvdB937sHeENT/+bAr4zzGt10e59/0HyuoMzb3zfOsvcCf9XU9E66fGY7zcQg7mYpZTTwNeCnEbGUMuoYHjnSTcAfUL7m3Eg5Un63ab8nIsZ9E9bQ/hHxzx3/vW7U458BdqLMjUL548PpEXE9ZQTyXV6GzLwCeCgibqHswJdn5rfHWPYxyte36yPiNuAgXvjwrunrDlJ29HtG5i67LPMMcCwlXA6lCcRmpHgocERE3NLUfgxwSBPgL9ds4F8jYgXlkq5PjXq819+TZcAGI98ksvwS4XPAVc3n+ZvNOr9A2b7PdKxyFWV0eA+wP/C3/Pzg40bKvvUgZTriFMq0zi9RDq6MWt9o3wIOjIgbKF/v76dMjdwFHBYRh9N93/sHYHnTx08Aj4/zGhP1PuDcJh9eagrr48A7m2/XVwF3j7diT3GWpMocEUtSZQaxJFVmEEtSZQaxJFVmEEtSZQaxpoWIWBgRw83PmTrbv9uc6vtSz5/TnJknrXMGsaaTe+k4y6s5g+2VYy8u9YaZeIqzpq9/ASIiNmpO+ngP5ToNW0bEuymXsxyinBRwHOV02UuAV1FOvwV+FuA/dxGZsU4ikSaDI2JNN18Cfrs5DXZXyunNr6Zc6Gaf5kI3T1BOLT6eck2BN1Gu8DWi20VkpNY4ItZ0cynlVNhllFPXoQw4/rXjdPUbKKfjrgd8HSAzb4uIkYvv9MIFnDSDOCLWtJKZyyjzwh/ghQutDAO/FhEj88UjF2zpvDDMTpTQhbEv4CS1whGxpqMvAL+XmfdFxLaUC75cClwbESOXYxy5lvDIxWLupcwfwwsXkZlNCfHfX6fVa8bxoj+SVJlTE5JUmUEsSZUZxJJUmUEsSZUZxJJUmUEsSZUZxJJU2f8HJpivCL1FkdcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Mode',data=poll_df,kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Did the poll results favour Clinton or Trump?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_1/tfp0k5355jxb501q_ckbc6m80000gn/T/ipykernel_64021/1762254217.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  avg = pd.DataFrame(poll_df.mean())\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trump</th>\n",
       "      <td>40.643890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinton</th>\n",
       "      <td>42.733903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>5.806011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>9.315068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "Trump      40.643890\n",
       "Clinton    42.733903\n",
       "Other       5.806011\n",
       "Undecided   9.315068"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg = pd.DataFrame(poll_df.mean())\n",
    "\n",
    "avg.drop(['Number of Observations','Question Iteration'],axis=0,inplace=True)\n",
    "\n",
    "avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_1/tfp0k5355jxb501q_ckbc6m80000gn/T/ipykernel_64021/2155388626.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  std = pd.DataFrame(poll_df.std())\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trump</th>\n",
       "      <td>23.566390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinton</th>\n",
       "      <td>25.298731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>5.009533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>6.253118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "Trump      23.566390\n",
       "Clinton    25.298731\n",
       "Other       5.009533\n",
       "Undecided   6.253118"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = pd.DataFrame(poll_df.std())\n",
    "\n",
    "std.drop(['Number of Observations','Question Iteration'],axis=0,inplace=True)\n",
    "\n",
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg.plot(yerr=std,kind='bar',legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The race seems to be really close, with results in favour of Clinton with a higher standard deviation than for Trump."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trump</th>\n",
       "      <td>40.643890</td>\n",
       "      <td>23.566390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinton</th>\n",
       "      <td>42.733903</td>\n",
       "      <td>25.298731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>5.806011</td>\n",
       "      <td>5.009533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>9.315068</td>\n",
       "      <td>6.253118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Average      Stdev\n",
       "Trump      40.643890  23.566390\n",
       "Clinton    42.733903  25.298731\n",
       "Other       5.806011   5.009533\n",
       "Undecided   9.315068   6.253118"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_avg = pd.concat([avg,std],axis=1)\n",
    "\n",
    "poll_avg.columns = ['Average','Stdev']\n",
    "\n",
    "poll_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Trump</th>\n",
       "      <th>Clinton</th>\n",
       "      <th>Other</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>940.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>41.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Likely Voters - Democrat</td>\n",
       "      <td>Internet</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Likely Voters - Republican</td>\n",
       "      <td>Internet</td>\n",
       "      <td>82.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Likely Voters - independent</td>\n",
       "      <td>Internet</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:10:06Z</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>43.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.investors.com/politics/ibd-tipp-pre...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pollster  Start Date    End Date  Entry Date/Time (ET)  \\\n",
       "0  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "1  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "2  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "3  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "4       IBD/TIPP  2016-11-04  2016-11-07  2016-11-08T12:10:06Z   \n",
       "\n",
       "   Number of Observations                   Population        Mode  Trump  \\\n",
       "0                   940.0                Likely Voters    Internet   41.0   \n",
       "1                     NaN     Likely Voters - Democrat    Internet    6.0   \n",
       "2                     NaN   Likely Voters - Republican    Internet   82.0   \n",
       "3                     NaN  Likely Voters - independent    Internet   38.0   \n",
       "4                  1107.0                Likely Voters  Live Phone   43.0   \n",
       "\n",
       "   Clinton  Other  Undecided  \\\n",
       "0     45.0    2.0        8.0   \n",
       "1     89.0    0.0        4.0   \n",
       "2      7.0    2.0        6.0   \n",
       "3     43.0    4.0        7.0   \n",
       "4     41.0    4.0        5.0   \n",
       "\n",
       "                                        Pollster URL  \\\n",
       "0  https://elections.huffingtonpost.com/pollster/...   \n",
       "1  https://elections.huffingtonpost.com/pollster/...   \n",
       "2  https://elections.huffingtonpost.com/pollster/...   \n",
       "3  https://elections.huffingtonpost.com/pollster/...   \n",
       "4  https://elections.huffingtonpost.com/pollster/...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "1  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "2  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "3  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "4  http://www.investors.com/politics/ibd-tipp-pre...  Nonpartisan        None   \n",
       "\n",
       "                                       Question Text  Question Iteration  \n",
       "0  As you may know, there will be a presidential ...                   1  \n",
       "1  As you may know, there will be a presidential ...                   1  \n",
       "2  As you may know, there will be a presidential ...                   1  \n",
       "3  As you may know, there will be a presidential ...                   1  \n",
       "4                                                NaN                   1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How did voter sentiment change over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Trump</th>\n",
       "      <th>Clinton</th>\n",
       "      <th>Other</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>940.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>41.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Likely Voters - Democrat</td>\n",
       "      <td>Internet</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Likely Voters - Republican</td>\n",
       "      <td>Internet</td>\n",
       "      <td>82.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Insights West</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:16:30Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Likely Voters - independent</td>\n",
       "      <td>Internet</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.insightswest.com/news/clinton-is-ah...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>As you may know, there will be a presidential ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>2016-11-08T12:10:06Z</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>43.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.investors.com/politics/ibd-tipp-pre...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pollster  Start Date    End Date  Entry Date/Time (ET)  \\\n",
       "0  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "1  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "2  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "3  Insights West  2016-11-04  2016-11-07  2016-11-08T12:16:30Z   \n",
       "4       IBD/TIPP  2016-11-04  2016-11-07  2016-11-08T12:10:06Z   \n",
       "\n",
       "   Number of Observations                   Population        Mode  Trump  \\\n",
       "0                   940.0                Likely Voters    Internet   41.0   \n",
       "1                     NaN     Likely Voters - Democrat    Internet    6.0   \n",
       "2                     NaN   Likely Voters - Republican    Internet   82.0   \n",
       "3                     NaN  Likely Voters - independent    Internet   38.0   \n",
       "4                  1107.0                Likely Voters  Live Phone   43.0   \n",
       "\n",
       "   Clinton  Other  Undecided  \\\n",
       "0     45.0    2.0        8.0   \n",
       "1     89.0    0.0        4.0   \n",
       "2      7.0    2.0        6.0   \n",
       "3     43.0    4.0        7.0   \n",
       "4     41.0    4.0        5.0   \n",
       "\n",
       "                                        Pollster URL  \\\n",
       "0  https://elections.huffingtonpost.com/pollster/...   \n",
       "1  https://elections.huffingtonpost.com/pollster/...   \n",
       "2  https://elections.huffingtonpost.com/pollster/...   \n",
       "3  https://elections.huffingtonpost.com/pollster/...   \n",
       "4  https://elections.huffingtonpost.com/pollster/...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "1  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "2  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "3  http://www.insightswest.com/news/clinton-is-ah...  Nonpartisan        None   \n",
       "4  http://www.investors.com/politics/ibd-tipp-pre...  Nonpartisan        None   \n",
       "\n",
       "                                       Question Text  Question Iteration  \\\n",
       "0  As you may know, there will be a presidential ...                   1   \n",
       "1  As you may know, there will be a presidential ...                   1   \n",
       "2  As you may know, there will be a presidential ...                   1   \n",
       "3  As you may know, there will be a presidential ...                   1   \n",
       "4                                                NaN                   1   \n",
       "\n",
       "   Difference  \n",
       "0        0.04  \n",
       "1        0.83  \n",
       "2       -0.75  \n",
       "3        0.05  \n",
       "4       -0.02  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df['Difference'] = (poll_df.Clinton - poll_df.Trump)/100\n",
    "\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference is Clinton - Trump. So a positive difference indicates a Clinton lead, and a negative difference indicates a Trump lead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Trump</th>\n",
       "      <th>Clinton</th>\n",
       "      <th>Other</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>30546.600000</td>\n",
       "      <td>45.200000</td>\n",
       "      <td>48.600000</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.034000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>734.125000</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>43.571429</td>\n",
       "      <td>4.642857</td>\n",
       "      <td>2.384615</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>-0.019286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>2016-11-02</td>\n",
       "      <td>1748.400000</td>\n",
       "      <td>40.823529</td>\n",
       "      <td>42.411765</td>\n",
       "      <td>6.941176</td>\n",
       "      <td>6.823529</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.015882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>2016-11-03</td>\n",
       "      <td>1386.250000</td>\n",
       "      <td>43.285714</td>\n",
       "      <td>46.428571</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.031429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>1144.461538</td>\n",
       "      <td>43.052632</td>\n",
       "      <td>43.789474</td>\n",
       "      <td>4.052632</td>\n",
       "      <td>4.578947</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>0.007368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Start Date  Number of Observations      Trump    Clinton     Other  \\\n",
       "262  2016-10-31            30546.600000  45.200000  48.600000  3.333333   \n",
       "263  2016-11-01              734.125000  45.500000  43.571429  4.642857   \n",
       "264  2016-11-02             1748.400000  40.823529  42.411765  6.941176   \n",
       "265  2016-11-03             1386.250000  43.285714  46.428571  4.428571   \n",
       "266  2016-11-04             1144.461538  43.052632  43.789474  4.052632   \n",
       "\n",
       "     Undecided  Question Iteration  Difference  \n",
       "262   3.333333            1.400000    0.034000  \n",
       "263   2.384615            1.285714   -0.019286  \n",
       "264   6.823529            1.470588    0.015882  \n",
       "265   2.000000            1.000000    0.031429  \n",
       "266   4.578947            1.052632    0.007368  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping polls by the start data\n",
    "poll_df = poll_df.groupby(['Start Date'],as_index=False).mean()\n",
    "\n",
    "poll_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Start Date'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "poll_df.plot('Start Date','Difference',figsize=(15,4),marker='o',linestyle='-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualisation indicates that Trump has a slight lead in the polls, on average. __Keep in mind that this is without accounting for the weights of different polls, demographics, and affiliations of the population polled.__ \n",
    "\n",
    "The latest start date in our grouped dataframe is 27-September-2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can we see an effect in the polls from the debates?\n",
    "\n",
    "Another interesting thing that we can look into, is what happened on the days leading up to the first debate, and afterwards. We know that the debate was on 26-September-2016, so we'll plot the trends for the month of September.\n",
    "\n",
    "To get a list of indices for September, we'll loop through the dataframe, and append any row with a September start date to the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First September Index: 209\n",
      "Last (Available) September Index: 231\n"
     ]
    }
   ],
   "source": [
    "row_in = 0\n",
    "xlimit = []\n",
    "\n",
    "for date in poll_df['Start Date']:\n",
    "    if date[0:7] == '2016-09':\n",
    "        xlimit.append(row_in)\n",
    "        row_in += 1\n",
    "    else:\n",
    "        row_in += 1\n",
    "        \n",
    "print(\"First September Index: \" + str(min(xlimit)))\n",
    "print(\"Last (Available) September Index: \"+ str(max(xlimit)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x283143880>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "poll_df.plot('Start Date','Difference',figsize=(15,4),marker='o',linestyle='-',xlim=(209,229))\n",
    "\n",
    "#Vertical line for debate date\n",
    "plt.axvline(x=228)"
   ]
  },
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   "metadata": {},
   "source": [
    "And what it looks like, is that the sentiment shifted slightly in favour of Trump, after the debate. Again, it's important to keep in mind that we're dealing with averages."
   ]
  }
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